Sökning: "xgboost"
Visar resultat 1 - 5 av 145 uppsatser innehållade ordet xgboost.
1. Optimizing Flight Ranking:A Machine Learning Approach : Applying Machine Learning to Upgrade Flight Sorting and User Experience
M1-uppsats, KTH/Hälsoinformatik och logistikSammanfattning : Flygresor.se, a leading flight comparison platform, uses machine learning to rankflights based on their likelihood of being clicked. The main goal of this project was toimprove this flight sorting to obtain a better user experience. The platform's existingmodel is based on a neural network approach and a limited set of features. LÄS MER
2. Predicting True Sepsis and Culture-positive Sepsis in Intensive Care Unit with Machine Learning Techniques
Master-uppsats, Lunds universitet/Matematisk statistikSammanfattning : Sepsis, a serious medical condition often leading to patients requiring intensive care, has prompted numerous scientists to employ mathematical techniques to aid in its diagnosis. This thesis uses logistic regression and a machine learning technique, XGBoost, to predict true sepsis (as opposed to sepsis mimics) and culture-positive sepsis (among true sepsis) in critical care using blood test results, physiological measurements and other patient characteristics. LÄS MER
3. On The Evaluation of District Heating Load Predictions
Master-uppsats, Lunds universitet/Institutionen för energivetenskaperSammanfattning : District Heating is a technology with the potential to enable a fossil-free society. However, to realize this potential, some improvements need to be made in order to improve District Heating operation at large, decrease losses in the systems, and thus increase the competitiveness of District Heating as a technology. LÄS MER
4. Explainable Artificial Intelligence and its Applications in Behavioural Credit Scoring
Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskapSammanfattning : Credit scoring is critical for banks to evaluate new loan applications and monitor existing customers. Machine learning has been extensively researched for this case; however, the adoption of machine learning methods is minimal in financial risk management. LÄS MER
5. Restaurant Daily Revenue Prediction : Utilizing Synthetic Time Series Data for Improved Model Performance
Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för beräkningsvetenskapSammanfattning : This study aims to enhance the accuracy of a demand forecasting model, XGBoost, by incorporating synthetic multivariate restaurant time series data during the training process. The research addresses the limited availability of training data by generating synthetic data using TimeGAN, a generative adversarial deep neural network tailored for time series data. LÄS MER